JCIF Feature Selection Method

Version 1.0.0 (6,01 KB) von U A Md Ehsan

This function implements the JCIF feature selection method

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Aktualisiert 24. Dez 2025

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This repository provides a MATLAB implementation of the Joint Conditional Mutual Information for Selecting Informative Feature (JCIF) selection criterion for supervised feature selection.

JCIF is an information-theoretic method that incrementally selects features by jointly considering:

Redundency handled using conditional mutual information, and Relevence via joint mutual information.

The method is designed for discrete-valued data and is particularly suitable for high-dimensional feature spaces.

Zitieren als

Ali, U. A. Md Ehsan, and Keisuke Kameyama. “Informative Band Subset Selection for Hyperspectral Image Classification Using Joint and Conditional Mutual Information.” 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2022, pp. 573–80, https://doi.org/10.1109/ssci51031.2022.10022154.

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Erstellt mit R2025b

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Version	Veröffentlicht	Versionshinweise
1.0.0	24. Dez 2025		Herunterladen

MLA	Ali, U. A. Md Ehsan, and Keisuke Kameyama. “Informative Band Subset Selection for Hyperspectral Image Classification Using Joint and Conditional Mutual Information.” 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2022, pp. 573–80, https://doi.org/10.1109/ssci51031.2022.10022154.
APA	Ali, U. A. M. E., & Kameyama, K. (2022). Informative Band Subset Selection for Hyperspectral Image Classification using Joint and Conditional Mutual Information. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 573–580). IEEE. Retrieved from http://dx.doi.org/10.1109/SSCI51031.2022.10022154
BibTeX	@inproceedings{Ali_2022, title={Informative Band Subset Selection for Hyperspectral Image Classification using Joint and Conditional Mutual Information}, url={http://dx.doi.org/10.1109/SSCI51031.2022.10022154}, DOI={10.1109/ssci51031.2022.10022154}, booktitle={2022 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Ali, U A Md Ehsan and Kameyama, Keisuke}, year={2022}, month=dec, pages={573–580} }